Potential of Heat Pump Technology with Model Predictive Control: A Game-Changer for Domestic Hot Water

Significance 

The European Union has experienced a significant growth in the heat pump (HP) market, with the adoption of air-source heat pumps as the main growth driver with an 81% increase in recent years (2015-2020). This growth is indicative of the increasing interest in utilizing heat pumps as a means to integrate renewable energy sources into the energy systems. In particular, the focus has shifted towards exploring the potential of heat pumps equipped with thermal energy storage (TES) systems in various applications, including space heating, domestic hot water (DHW), and a combination of both. In multi-family residential complexes, the demand for decentralized DHW systems has been on the rise due to its numerous benefits such as energy conservation, improved energy efficiency, and more relaxed Legionella control regulations. The promising trend of domestic hot water heat pumps (HWHP) is evident in the significant increase in sales in Germany, which surged by over 15% in 2020-2021. To harness the potential of HWHPs for renewable energy integration, researchers have turned to model predictive control (MPC) as a promising approach, outshining traditional rule-based methods like hysteresis control (HYS). MPC’s versatility in handling multiple objectives and its focus on incentives such as day-ahead spot market prices have made it a subject of great interest in the literature. While MPC has been widely explored in simulation studies, experimental studies have been relatively scarce, particularly in the context of HWHPs with TES systems.  To this account, a new research study published in Energy & Buildings by Christian Baumann, Gerhard Huber, Jovan Alavanja, Markus Preißinger, and Peter Kepplinger from the Vorarlberg University of Applied Sciences in Austria addressed the crucial need for experimental validation of MPC’s potential for DHW use in heat pumps under realistic conditions.

The research team developed an experimental setup featuring an off-the-shelf 200-liter air-source HWHP. The control system of the HP was based on a hysteresis control with a 7 Kelvin threshold, serving as a benchmark for comparison with the MPC approach. To facilitate the experimental study, the HWHP was equipped with an array of sensors and actuators, enabling the collection of critical data related to temperatures, power consumption, volume flow rate, and hot water demand. The incentive for MPC control was derived from the day-ahead stock market prices, which provided a real-world pricing signal for optimizing the HP operation. The optimization routine was conducted at 15-minute intervals to determine the optimal switching state, considering a prediction horizon of up to 24 hours based on the available incentives.

The authors’ experimental setup encountered some challenges related to the HP’s control system. Specifically, there were delays in the start of HP operation and issues related to operation within a temperature dead band. To address these challenges, the researchers implemented a pre-switching strategy to mitigate delays and reset the HP’s control memory to ensure operation within the dead band.

The authors assessed the potential benefits of MPC compared to traditional HYS control in the context of DHW use with HWHPs. The comparison was based on various parameters, including electric energy consumption, total DHW demand, coefficient of performance (COP), costs per unit electricity and heat, total electricity costs, total operation time, and HP start-ups. They demonstrated that the MPC scenarios consistently outperformed the HYS control. Specifically, MPC led to a reduction in electric energy consumption by up to 20%, along with a decrease in total operation time by up to 33%. These important improvements translated to an increase in efficiency of up to 24%. Furthermore, the costs per unit electricity and heat decreased by 17% and 33%, respectively, in the MPC scenarios compared to HYS control. They also assessed efficiency and comfort conditions, with the COP increasing by approximately 13% in the MPC scenarios. The reduction in operation time and power consumption resulted in lower thermal well temperatures, which contributed to improved efficiency.

In conclusion, the authors’ findings highlighted that MPC can lead to substantial energy savings, cost reductions, and enhanced system efficiency, all while maintaining user comfort. These outcomes are crucial steps towards achieving more sustainable and energy-efficient domestic hot water systems, particularly in the context of heat pumps with thermal energy storage.

Potential of Heat Pump Technology with Model Predictive Control: A Game-Changer for Domestic Hot Water - Advances in Engineering

About the author

CHRISTIAN BAUMANN received the B.Eng. in Energy Engineering from the University of Applied Sciences in Kempten, Germany, in 2017, and the M.Sc. degree in Energy Technology and Energy Economics from the University of Applied Sciences Vorarlberg, Austria, in 2020. He is currently pursuing the Ph.D. degree with the University of Innsbruck, where he specializes in the application and utilization of heat pump flexibilities. Since 2020, he has been a researcher within the Josef Ressel Center for Intelligent Thermal Energy Systems and the Research Center Energy at the University of Applied Sciences Vorarlberg, Austria. His current research interests include the experimental characterization of heat pump systems and their optimal integration in distributed energy systems to enable demand side management.

About the author

JOVAN ALAVANJA received a B.Sc. in Computer Science from the University of Applied Sciences in Vorarlberg, Austria, in 2022. In 2020, he joined the Josef Ressel Center for Intelligent Thermal Energy Systems and the Research Center Energy at the University of Applied Sciences Vorarlberg, Austria, as a research assistant. His research includes the implementation of test environments for software testing and validation in the field of demand-side management.

About the author

MARKUS PREIßINGER is head of research at Vorarlberg University of Applied Sciences. He is also head of the Research Center Energy, the Josef Ressel Center for Intelligent Thermal Energy System and holds the illwerke vkw endowed professorship for energy efficiency. He is an expert in thermal engineering with a PhD from the University of Bayreuth, Germany. Further, he sees himself as mentor for young scientist helping them to find and go their way in research and development.

About the author

GERHARD HUBER is a research associate at the Research Center Energy at the Vorarlberg University for Applied Sciences, Austria. He received his M.Sc. in Energy and Environmental Management at the Burgenland University of Applied Sciences, Austria, in 2007. He subsequently worked as an energy engineer in the food industry before joining the Vorarlberg University of Applied Science in 2012, where he focuses on industrial demand side management.

About the author

PETER KEPPLINGER completed his studies in Applied Mathematics at the University of Vienna in 2012. He received his Doctor of Technical Sciences in 2019 at the University of Innsbruck on autonomous demand side management of electric hot water storage systems. Starting from 2013, he has been a research assistant at the Research Center Energy of the Vorarlberg University of Applied Sciences, becoming head of the research group Energy Systems and Components in 2017. He and his team have been active in various research projects, applying methods from mathematical optimization, simulation and data science to problems within the field of demand side management.

Reference

Christian Baumann, Gerhard Huber, Jovan Alavanja, Markus Preißinger, Peter Kepplinger, Experimental validation of a state-of-the-art model predictive control approach for demand side management with a hot water heat pump, Energy and Buildings, Volume 285, 2023, 112923,

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